Search results for: encrypted traffic classification

Authors: Jin Fan, M. Ala Saadeghvaziri

Abstract:

Unmanned aerial vehicles (UAVs), also referred to as drones, equipped with various kinds of advanced detecting or surveying systems, are effective and low-cost in data acquisition, data delivery and sharing, which can benefit the building of infrastructures. This paper will give an overview of applications of drones in planning, designing, construction and maintenance of infrastructures. The drone platform, detecting and surveying systems, and post-data processing systems will be introduced, followed by cases with details of the applications. Challenges from different aspects will be addressed. Opportunities of drones in infrastructure include but not limited to the following. Firstly, UAVs equipped with high definition cameras or other detecting equipment are capable of inspecting the hard to reach infrastructure assets. Secondly, UAVs can be used as effective tools to survey and map the landscape to collect necessary information before infrastructure construction. Furthermore, an UAV or multi-UVAs are useful in construction management. UVAs can also be used in collecting roads and building information by taking high-resolution photos for future infrastructure planning. UAVs can be used to provide reliable and dynamic traffic information, which is potentially helpful in building smart cities. The main challenges are: limited flight time, the robustness of signal, post data analyze, multi-drone collaboration, weather condition, distractions to the traffic caused by drones. This paper aims to help owners, designers, engineers and architects to improve the building process of infrastructures for higher efficiency and better performance.

Keywords: bridge, construction, drones, infrastructure, information

Procedia PDF Downloads 126

Authors: Ahmad Samim Ranjbar, Shoshi Mizokami

Abstract:

Kabul is the heart of political, commercial, cultural, educational and social life in Afghanistan and the Kabul fifth fastest growing city in the world, since 2001 with the establishment of new government Lack of adequate employment opportunities and basic utility services in remote provinces have prompted people to move to Kabul and other urban areas. From 2001 to the present, a rapid increase in population, and also less income of the people most of residence tend to use public transport, especially buses, however there is no proper bus system exist in Kabul city, because of wars, from 1992 to 2001 Kabul suffered damage and destruction of its transportation facilities including pavements, sidewalks, traffic circles, drainage systems, traffic signs and signals, trolleybuses and almost all of the public transit buses (e.g. Millie bus). This research is a primary and very important phase into Kabul city transportation and especially an initial and important step toward using large bus in Kabul city, which the main purpose of this research is to find the demand of Kabul city residence for public transport (Large Bus) and compare it with the actual supply from government. Finding of this research shows that the demand of Kabul city residence for the public transport (Large Bus) exceed the supply from the government, means that current public transportation (Large Bus) is not sufficient to serve people of Kabul city, it is mentionable that according to this research there is no need to build a new road or exclusive way for bus, this research propose to government for investment on the public transportation and exceed the number of large buses to can handle the current demand for public transport.

Keywords: transportation, planning, public transport, large bus, Kabul, Afghanistan

Procedia PDF Downloads 299

Authors: Dangut Maren David, Skaf Zakwan

Abstract:

Adequate monitoring of vehicle component in other to obtain high uptime is the goal of predictive maintenance, the major challenge faced by businesses in industries is the significant cost associated with a delay in service delivery due to system downtime. Most of those businesses are interested in predicting those problems and proactively prevent them in advance before it occurs, which is the core advantage of Prognostic Health Management (PHM) application. The recent emergence of industry 4.0 or industrial internet of things (IIoT) has led to the need for monitoring systems activities and enhancing system-to-system or component-to- component interactions, this has resulted to a large generation of data known as big data. Analysis of big data represents an increasingly important, however, due to complexity inherently in the dataset such as imbalance classification problems, it becomes extremely difficult to build a model with accurate high precision. Data-driven predictive modeling for condition-based maintenance (CBM) has recently drowned research interest with growing attention to both academics and industries. The large data generated from industrial process inherently comes with a different degree of complexity which posed a challenge for analytics. Thus, imbalance classification problem exists perversely in industrial datasets which can affect the performance of learning algorithms yielding to poor classifier accuracy in model development. Misclassification of faults can result in unplanned breakdown leading economic loss. In this paper, an advanced approach for handling imbalance classification problem is proposed and then a prognostic model for predicting aircraft component replacement is developed to predict component replacement in advanced by exploring aircraft historical data, the approached is based on hybrid ensemble-based method which improves the prediction of the minority class during learning, we also investigate the impact of our approach on multiclass imbalance problem. We validate the feasibility and effectiveness in terms of the performance of our approach using real-world aircraft operation and maintenance datasets, which spans over 7 years. Our approach shows better performance compared to other similar approaches. We also validate our approach strength for handling multiclass imbalanced dataset, our results also show good performance compared to other based classifiers.

Keywords: prognostics, data-driven, imbalance classification, deep learning

Procedia PDF Downloads 176

Authors: Ling Qi, Matloob Khushi, Josiah Poon

Abstract:

This paper introduces a pre-trained Masked Candlestick Model (MCM) for trading time-series data. The pre-trained model is based on three core designs. First, we convert trading price data at each data point as a set of normalized elements and produce embeddings of each element. Second, we generate a masked sequence of such embedded elements as inputs for self-supervised learning. Third, we use the encoder mechanism from the transformer to train the inputs. The masked model learns the contextual relations among the sequence of embedded elements, which can aid downstream classification tasks. To evaluate the performance of the pre-trained model, we fine-tune MCM for three different downstream classification tasks to predict future price trends. The fine-tuned models achieved better accuracy rates for all three tasks than the baseline models. To better analyze the effectiveness of MCM, we test the same architecture for three currency pairs, namely EUR/GBP, AUD/USD, and EUR/JPY. The experimentation results demonstrate MCM’s effectiveness on all three currency pairs and indicate the MCM’s capability for signal extraction from trading data.

Keywords: masked language model, transformer, time series prediction, trading prediction, embedding, transfer learning, self-supervised learning

Procedia PDF Downloads 130

Authors: G. C. Tikkiwal, Mukesh Upadhyay

Abstract:

During the surveillance operations at war or peace time, the Radar operator gets a scatter of targets over the screen. This may be a tracked vehicle like tank vis-à-vis T72, BMP etc, or it may be a wheeled vehicle like ALS, TATRA, 2.5Tonne, Shaktiman or moving army, moving convoys etc. The Radar operator selects one of the promising targets into Single Target Tracking (STT) mode. Once the target is locked, the operator gets a typical audible signal into his headphones. With reference to the gained experience and training over the time, the operator then identifies the random target. But this process is cumbersome and is solely dependent on the skills of the operator, thus may lead to misclassification of the object. In this paper we present a technique using mathematical and statistical methods like Fast Fourier Transformation (FFT) and Principal Component Analysis (PCA) to identify the random objects. The process of classification is based on transforming the audible signature of target into music octave-notes. The whole methodology is then automated by developing suitable software. This automation increases the efficiency of identification of the random target by reducing the chances of misclassification. This whole study is based on live data.

Keywords: radar target, fft, principal component analysis, eigenvector, octave-notes, dsp

Procedia PDF Downloads 347

Authors: Ayman M. Othman, Hassan Y. Ahmed, Tallat A. Ali

Abstract:

Inadequate maintenance programs and funds allocated for highway networks in the developed countries have led to fast deterioration of road side elements. Therefore, this research focuses on developing a performance model for road side elements periodic maintenance activities. Road side elements that receive periodic maintenance include; earthen shoulder, road signs and traffic markings. Using the level of service concept, the developed model can determine the optimal periodic maintenance intervals for those elements based on a selected level of service suitable with the available periodic maintenance budget. Data related to time periods for progressive deterioration stages for the chosen elements were collected. Ten maintenance experts in Aswan, Sohag and Assiut cities were interviewed for that purpose. Time in months related to 10%, 25%, 40%, 50%, 75%, 90% and 100% deterioration of each road side element was estimated based on the experts opinion. Least square regression analysis has shown that a power function represents the best fit for earthen shoulders edge drop-off and damage of road signs with time. It was also evident that, the progressive dirtiness of road signs could be represented by a quadratic function an a linear function could represent the paint degradation nature of both traffic markings and road signs. Actual measurements of earthen shoulder edge drop-off agree considerably with the developed model.

Keywords: deterioration, level of service, periodic maintenance, performance model, road side element

Procedia PDF Downloads 574

Authors: Jayashree Katti, Harsh Warkari, Prachi Yadav, Bhagyashri Chaudhari

Abstract:

Approximately ten percent of individuals globally suffer from kidney stones, which can cause major side effects, including renal damage and blockage of the urinary tract. Traditional detection techniques depend on the manual evaluation of CT or X-ray images, which is not easy and may contain errors. With the aim to enhance kidney stone detection using medical imaging, this research explores various machine learning methods, such as Convolutional Neural Networks (CNN). By reviewing many machine learning algorithms, like ensemble techniques, Decision Tree, Random Forest, and Support Vector Machines (SVM), this study shows that machine learning tends to improve accuracy and reduce kidney stone detection time. According to the results of the earlier research, ensemble methods produced a classification accuracy of 97.95%, whereas the Decision Tree Classifier obtained an F1 score of 85.3%. Ensemble approaches gave a classification accuracy of 97.95%. Advanced techniques utilizing transfer learning, such as ALEXNET, achieved an accuracy rate of 96%.

Keywords: kidney stones, machine learning, medical imaging, CNN, transfer learning, decision tree, ensemble methods, random forest, SVM, ALEXNET

Procedia PDF Downloads 4

Authors: Mohamed Mouloud Haddak, Bouthayna Hayou

Abstract:

Road accidents in French overseas territories have been understudied, with relevant data often collected late and incompletely. Although these territories account for only 3% to 4% of road traffic injuries in France, their unique characteristics merit closer attention. Despite lower mobility and, consequently, lower exposure to road risks, the actual road risk in Overseas France is as high or even higher than in Metropolitan France. Significant disparities exist not only between Metropolitan France and Overseas territories but also among the overseas territories themselves. The varying population densities in these regions do not fully explain these differences, as each territory has its own distinct vulnerabilities and road safety challenges. This analysis, based on BAAC data files from 2005 to 2018 for both Metropolitan France and the overseas departments and regions, examines key variables such as gender, age, type of road user, type of obstacle hit, type of trip, road category, traffic conditions, weather, and location of accidents. Logistic regression models were built for each region to investigate the risk factors associated with fatal road accidents, focusing on the probability of being killed versus injured. Due to insufficient data, Mayotte and the Overseas Communities (French Polynesia and New Caledonia) were not included in the models. The findings reveal that road safety is worse in the overseas territories compared to Metropolitan France, particularly for vulnerable road users such as pedestrians and motorized two-wheelers. These territories present an accident profile that sits between that of Metropolitan France and middle-income countries. A pressing need exists to standardize accident data collection between Metropolitan and Overseas France to allow for more detailed comparative analyses. Further epidemiological studies could help identify the specific road safety issues unique to each territory, particularly with regards to socio-economic factors such as social cohesion, which may influence road safety outcomes. Moreover, the lack of data on new modes of travel, such as electric scooters, and the absence of socio-economic details of accident victims complicate the evaluation of emerging risk factors. Additional research, including sociological and psychosocial studies, is essential for understanding road users' behavior and perceptions of road risk, which could also provide valuable insights into accident trends in peri-urban areas in France.

Keywords: multivariate logistic regression, french overseas regions, road safety, road traffic accidents, territorial inequalities

Procedia PDF Downloads 16

Authors: Kothuri Sriraman, Mattupalli Komal Teja

Abstract:

In this paper, a novel algorithm is proposed for the recognition of hulls in a hand written images that might be irregular or digit or character shape. Identification of objects and internal objects is quite difficult to extract, when the structure of the image is having bulk of clusters. The estimation results are easily obtained while going through identifying the sub-regional objects by using the SASK algorithm. Focusing mainly to recognize the number of internal objects exist in a given image, so as it is shadow-free and error-free. The hard clustering and density clustering process of obtained image rough set is used to recognize the differentiated internal objects, if any. In order to find out the internal hull regions it involves three steps pre-processing, Boundary Extraction and finally, apply the Hull Detection system. By detecting the sub-regional hulls it can increase the machine learning capability in detection of characters and it can also be extend in order to get the hull recognition even in irregular shape objects like wise black holes in the space exploration with their intensities. Layered hulls are those having the structured layers inside while it is useful in the Military Services and Traffic to identify the number of vehicles or persons. This proposed SASK algorithm is helpful in making of that kind of identifying the regions and can useful in undergo for the decision process (to clear the traffic, to identify the number of persons in the opponent’s in the war).

Keywords: chain code, Hull regions, Hough transform, Hull recognition, Layered Outline Extraction, SASK algorithm

Procedia PDF Downloads 352

Authors: Felipe A. Guth, Shane Ward, Kevin McDonnell

Abstract:

In the precision agriculture area, one of the main tasks is the automated detection of diseases in crops. Machine Learning algorithms have been studied in recent decades for such tasks in view of their potential for improving economic outcomes that automated disease detection may attain over crop fields. The latest generation of deep learning convolution neural networks has presented significant results in the area of image classification. In this way, this work has tested the implementation of an architecture of deep learning convolution neural network for the detection of diseases in different types of crops. A data augmentation strategy was used to meet the requirements of the algorithm implemented with a deep learning framework. Two test scenarios were deployed. The first scenario implemented a neural network under images extracted from a controlled environment while the second one took images both from the field and the controlled environment. The results evaluated the generalisation capacity of the neural networks in relation to the two types of images presented. Results yielded a general classification accuracy of 59% in scenario 1 and 96% in scenario 2.

Keywords: convolutional neural networks, deep learning, disease detection, precision agriculture

Procedia PDF Downloads 260

Authors: David Pham, Yongfeng Zhang

Abstract:

Graphs are an important structure for data storage and computation. Recent years have seen the success of deep learning on graphs such as Graph Neural Networks (GNN) on various data mining and machine learning tasks. However, most of the deep learning models on graphs cannot easily explain their predictions and are thus often labelled as “black boxes.” For example, Graph Attention Network (GAT) is a frequently used GNN architecture, which adopts an attention mechanism to carefully select the neighborhood nodes for message passing and aggregation. However, it is difficult to explain why certain neighbors are selected while others are not and how the selected neighbors contribute to the final classification result. In this paper, we present a graph learning model called Explainable Graph Attention Network (XGAT), which integrates graph attention modeling and explainability. We use a single model to target both the accuracy and explainability of problem spaces and show that in the context of graph attention modeling, we can design a unified neighborhood selection strategy that selects appropriate neighbor nodes for both better accuracy and enhanced explainability. To justify this, we conduct extensive experiments to better understand the behavior of our model under different conditions and show an increase in both accuracy and explainability.

Keywords: explainable AI, graph attention network, graph neural network, node classification

Procedia PDF Downloads 204

Authors: Teo Hao Jing, Shane Ho Ken Wae, Lee Jin Yu, Burra Venkata Durga Kumar

Abstract:

Current distributed healthcare systems face the challenge of interoperability of health data. Storing electronic health records (EHR) in local databases causes them to be fragmented. This problem is aggravated as patients visit multiple healthcare providers in their lifetime. Existing solutions are unable to solve this issue and have caused burdens to healthcare specialists and patients alike. Blockchain technology was found to be able to increase the interoperability of health data by implementing digital access rules, enabling uniformed patient identity, and providing data aggregation. Consortium blockchain was found to have high read throughputs, is more trustworthy, more secure against external disruptions and accommodates transactions without fees. Therefore, this paper proposes a blockchain-based model for data management applications. In this model, a consortium blockchain is implemented by using a delegated proof of stake (DPoS) as its consensus mechanism. This blockchain allows collaboration between users from different organizations such as hospitals and medical bureaus. Patients serve as the owner of their information, where users from other parties require authorization from the patient to view their information. Hospitals upload the hash value of patients’ generated data to the blockchain, whereas the encrypted information is stored in a distributed cloud storage.

Keywords: blockchain technology, data management applications, healthcare, interoperability, delegated proof of stake

Procedia PDF Downloads 139

Authors: Joseph Wolff, Jeffrey Eilbott

Abstract:

Autism Spectrum Disorders (ASD) are a spectrum of social disorders characterized by deficits in social communication, verbal ability, and interaction that can vary in severity. In recent years, researchers have used magnetic resonance imaging (MRI) to help detect how neural patterns in individuals with ASD differ from those of neurotypical (NT) controls for classification purposes. This study analyzed the classification of ASD within males and females using functional MRI data. Functional connectivity (FC) correlations among brain regions were used as feature inputs for machine learning algorithms. Analysis was performed on 558 cases from the Autism Brain Imaging Data Exchange (ABIDE) I dataset. When trained specifically on females, the algorithm underperformed in classifying the ASD subset of our testing population. Although the subject size was relatively smaller in the female group, the manual matching of both male and female training groups helps explain the algorithm’s bias, indicating the altered sex abnormalities in functional brain networks compared to typically developing peers. These results highlight the importance of taking sex into account when considering how generalizations of findings on males with ASD apply to females.

Keywords: autism spectrum disorder, machine learning, neuroimaging, sex differences

Procedia PDF Downloads 212

Authors: A. Degale Desta, Cheng Jian

Abstract:

Background: Vehicle detection accuracy is critical to intelligent transportation systems and autonomous driving. The state-of-the-art object identification technology YOLOv8 has shown significant gains in efficiency and detection accuracy. This study uses the BDD100K dataset, which is renowned for its extensive and varied annotations, to assess how well YOLOv8 performs in vehicle detection. Objectives: The primary objective of this research is to assess YOLOv8's performance in intelligent transportation system vehicle identification and its ability to accurately identify cars in urban environments for safety prioritization. Methods: The primary objective of this research is to assess YOLOv8's performance in intelligent transportation system vehicle identification and its ability to accurately identify cars in urban environments for safety prioritization. Results: The results show that YOLOv8 achieves high mAP, recall, precision, and F1-score values, indicating state-of-the-art performance. This suggests that YOLOv8 can identify cars in complex urban environments with a high degree of accuracy and reliable results in a variety of traffic scenarios. Conclusion: The results indicate that YOLOv8 is a useful tool for enhancing vehicle detection accuracy in intelligent transportation systems, hence advancing urban public safety and security. The model's demonstrated performance shows how well it may be incorporated into autonomous driving applications to improve situational awareness and responsiveness.

Keywords: vehicle detection, YOLOv8, BDD100K, object detection, deep learning

Procedia PDF Downloads 13

Authors: Zakari Mustapha, Clinton Aigbavboa, Wellington Didi Thwala

Abstract:

Majority of the incidents and accidents in complex high-risk systems that exist in the construction industry and other sectors have been attributed to unsafe acts of workers. The purpose of this paper was to asses Human Factors Analysis and Classification System (HFACS) in construction accident prevention. The study was conducted through the use of secondary data from journals, books and internet to achieve the objective of the study. The review of literature looked into details of different views from different scholars about HFACS framework in accidents investigations. It further highlighted on various sections or disciplines of accident occurrences in human performance within the construction. The findings from literature review showed that unsafe acts of a worker and unsafe working conditions are the two major causes of accident in the construction industry.Most significant factor in the cause of site accident in the construction industry is unsafe acts of a worker. The findings also show how the application of HFACS framework in the investigation of accident will lead to the identification of common trends. Further findings show that provision for the prevention of accident will be made based on past accident records to identify and prioritize where intervention is needed within the construction industry.

Keywords: accident, construction, HFACS, unsafe acts

Procedia PDF Downloads 325

Authors: Bouthayna Hayou, Mohamed Mouloud Haddak

Abstract:

In road accidents in French overseas territories have been understudied, with relevant data often collected late and incompletely. Although these territories account for only 3% to 4% of road traffic injuries in France, their unique characteristics merit closer attention. Despite lower mobility and, consequently, lower exposure to road risks, the actual road risk in Overseas France is as high or even higher than in Metropolitan France. Significant disparities exist not only between Metropolitan France and Overseas territories but also among the overseas territories themselves. The varying population densities in these regions do not fully explain these differences, as each territory has its own distinct vulnerabilities and road safety challenges. This analysis, based on BAAC data files from 2005 to 2018 for both Metropolitan France and the overseas departments and regions, examines key variables such as gender, age, type of road user, type of obstacle hit, type of trip, road category, traffic conditions, weather, and location of accidents. Logistic regression models were built for each region to investigate the risk factors associated with fatal road accidents, focusing on the probability of being killed versus injured. Due to insufficient data, Mayotte and the Overseas Communities (French Polynesia and New Caledonia) were not included in the models. The findings reveal that road safety is worse in the overseas territories compared to Metropolitan France, particularly for vulnerable road users such as pedestrians and motorized two-wheelers. These territories present an accident profile that sits between that of Metropolitan France and middle-income countries. A pressing need exists to standardize accident data collection between Metropolitan and Overseas France to allow for more detailed comparative analyses. Further epidemiological studies could help identify the specific road safety issues unique to each territory, particularly with regard to socio-economic factors such as social cohesion, which may influence road safety outcomes. Moreover, the lack of data on new modes of travel, such as electric scooters, and the absence of socio-economic details of accident victims complicate the evaluation of emerging risk factors. Additional research, including sociological and psychosocial studies, is essential for understanding road users' behavior and perceptions of road risk, which could also provide valuable insights into accident trends in peri-urban areas in France.

Keywords: multivariate logistic regression, overseas France, road safety, road traffic accident, territorial inequalities

Procedia PDF Downloads 15

Authors: Kishor Chandra Kandpal, Amit Kumar

Abstract:

The Indian Himalaya region harbours approximately 1748 plants of medicinal importance, and as per International Union for Conservation of Nature (IUCN), the 112 plant species among these are threatened and endangered. To ease the pressure on these plants, the government of India is encouraging its in-situ cultivation. The Saussurea costus, Valeriana jatamansi, and Picrorhiza kurroa have also been prioritized for large scale cultivation owing to their market demand, conservation value and medicinal properties. These species are found from 1000 m to 4000 m elevation ranges in the Indian Himalaya. Identification of these plants in the field requires taxonomic skills, which is one of the major bottleneck in the conservation and management of these plants. In recent years, Hyperspectral remote sensing techniques have been precisely used for the discrimination of plant species with the help of their unique spectral signatures. In this background, a spectral library of the above 03 medicinal plants was prepared by collecting the spectral data using a handheld spectroradiometer (325 to 1075 nm) from farmer’s fields of Himachal Pradesh and Uttarakhand states of Indian Himalaya. The Random forest (RF) model was implied on the spectral data for the classification of the medicinal plants. The 80:20 standard split ratio was followed for training and validation of the RF model, which resulted in training accuracy of 84.39 % (kappa coefficient = 0.72) and testing accuracy of 85.29 % (kappa coefficient = 0.77). This RF classifier has identified green (555 to 598 nm), red (605 nm), and near-infrared (725 to 840 nm) wavelength regions suitable for the discrimination of these species. The findings of this study have provided a technique for rapid and onsite identification of the above medicinal plants in the field. This will also be a key input for the classification of hyperspectral remote sensing images for mapping of these species in farmer’s field on a regional scale. This is a pioneer study in the Indian Himalaya region for medicinal plants in which the applicability of hyperspectral remote sensing has been explored.

Keywords: himalaya, hyperspectral remote sensing, machine learning; medicinal plants, random forests

Procedia PDF Downloads 204

Authors: Blake Milton, Georgette Goode, Elias Sachawars, Virgil Chan, Jason Dizon, Christopher Oldmeadow, Garbor Major

Abstract:

Fragility fractures of the pelvis (FFP) are a common and increasing fracture type in our ageing population. A novel grading classification system developed for FFP by Rommens and Hofmann attributes a severity score related to degree of pelvic ring involvement, which is assessed on CT imaging. The purpose of this study is to assess the relationship between radiologist-assigned FFP grading and patient mortality in conservatively managed patients. This retrospective review identified consecutive 100 patients aged ≥ 65 years at time of FFP. The Rommens-Hofmann severity grading was allocated to these injuries by 2 radiology trainees and a consultant radiologist. Five-year survival was determined from review of patient medical records. Patient medical records were also analysed to account for possible confounding factors including age, gender, comorbidities (Charlson score) and relative socio-economic disadvantage (SEIFA decile). Suitable FFP’s (n = 99) were classified by increasing severity by increasing severity grades: Type I (43% (n = 43)), Type II (33% (n = 33)), Type III (13% (n = 13)), Type IV (10% (n = 10)). No significant differences in survival were found between fracture groups, which persisted when also adjusting for age, gender, Charslon score or SEIFA decile. There was a lack of evidence to suggest a relationship between CT-based fracture grading and patient survival, even when accounting for the listed possible confounding factors. This may be due to small sample size or possible study biases, or possible heterogeneity within the population not adequately captured with available metrics. Given that no difference in mortality has been identified between FFP grades in conservatively managed patients, further research is important to assess mortality benefit in an operative patient population.

Keywords: fragility fractures, fracture classification, pelvic CT, pelvic fracture, pelvic ring fracture

Procedia PDF Downloads 8

Authors: Cheng-Yu Tsai, Wen-Te Liu, Chen-Chen Lo, Yin-Tzu Lin, Kang Lo

Abstract:

Background: Driving fatigue related to inadequate or disordered sleep accounts for a major percentage of traffic accidents. Obstructive sleep apnea syndrome (OSAS) is a common respiratory disorder during sleep. However, the effects of OSAS severity on driving drowsiness remain unclear. Objective: The aim of this study is to investigate the relationship between OSAS severity and driving fatigue. Methodologies: The physical condition while driving was obtained from the questionnaires to classify the state of driving fatigue. OSAS severity was quantified as the polysomnography, and the mean hourly number of greater than 3% dips in oxygen saturation during examination in a hospital in New Taipei City (Taiwan). The severity of OSAS was diagnosed by the apnea and hypopnea index (AHI) with the American Academy of Sleep Medicine (AASM) guideline. The logistic regression model was used to examine the associations after adjusted age, gender, neck circumstance, waist circumstance, and body mass index (BMI). Results: There were 880 subjects recruited in this study, who had been done polysomnography for evaluating severity for OSAS as well as completed the driver condition questionnaire. 752 subjects were diagnosed with OSA, and 484 subjects had fatigue driving behavior in the past week. Patients diagnosed with OSAS had a 9.42-fold higher odds ratio (p < 0.01, 95% CI = 5.41 – 16.42) of driving drowsiness for cohorts with a normal degree. Conclusion: We observe the considerable correlation between OSAS and driving fatigue. For the purpose of promoting traffic safety, OSAS should be monitored and treated.

Keywords: obstructive sleep apnea syndrome, driving fatigue, polysomnography, apnea and hypopnea index

Procedia PDF Downloads 135

Authors: H. S. Sathish, H. S. Jagadeesh, Skanda Kumar

Abstract:

Journey time and delay study is used to evaluate the quality of service, the travel time and study can also be used to evaluate the quality of traffic movement along the route and to determine the location types and extent of traffic delays. Components of delay are boarding and alighting, issue of tickets, other causes and distance between each stops. This study investigates the total journey time required to travel along the stretch and the influence the delays. The route starts from Kempegowda Bus Station to Yelahanka Satellite Station of Bangalore City. The length of the stretch is 16.5 km. Modal split analysis has been done for this stretch. This stretch has elevated highway connecting to Bangalore International Airport and the extension of metro transit stretch. From the regression analysis of total journey time it is affected by delay due to boarding and alighting moderately, Delay due to issue of tickets affects the journey time to a higher extent. Some of the delay factors affecting significantly the journey time are evident from F-test at 10 percent level of confidence. Along this stretch work trips are more prevalent as indicated by O-D study. Modal shift analysis indicates about 70 percent of commuters are ready to shift from current system to Metro Rail System. Metro Rail System carries maximum number of trips compared to private mode. Hence Metro is a highly viable choice of mode for Bangalore Metropolitan City.

Keywords: delay, journey time, modal choice, regression analysis

Procedia PDF Downloads 498

Authors: Zaoui Ismahene, Bahri Sidi Mohamed, Abbassa Nadira

Abstract:

Diabetic Retinopathy (DR) is a leading cause of vision impairment and blindness among individuals with diabetes. Early diagnosis is crucial for effective treatment, yet current diagnostic methods rely heavily on manual analysis of retinal images, which can be time-consuming and prone to subjectivity. This research proposes an automated system for the detection of DR using Jacobi wavelet-based feature extraction combined with Support Vector Machines (SVM) for classification. The integration of wavelet analysis with machine learning techniques aims to improve the accuracy, efficiency, and reliability of DR diagnosis. In this study, retinal images are preprocessed through normalization, resizing, and noise reduction to enhance the quality of the images. The Jacobi wavelet transform is then applied to extract both global and local features, effectively capturing subtle variations in retinal images that are indicative of DR. These extracted features are fed into an SVM classifier, known for its robustness in handling high-dimensional data and its ability to achieve high classification accuracy. The SVM classifier is optimized using parameter tuning to improve performance. The proposed methodology is evaluated using a comprehensive dataset of retinal images, encompassing a range of DR severity levels. The results show that the proposed system outperforms traditional wavelet-based methods, demonstrating significantly higher accuracy, sensitivity, and specificity in detecting DR. By leveraging the discriminative power of Jacobi wavelet features and the robustness of SVM, the system provides a promising solution for the automatic detection of DR, which could assist ophthalmologists in early diagnosis and intervention, ultimately improving patient outcomes. This research highlights the potential of combining wavelet-based image processing with machine learning for advancing automated medical diagnostics.

Keywords: iabetic retinopathy (DR), Jacobi wavelets, machine learning, feature extraction, classification

Procedia PDF Downloads 0

Authors: Sadia Basar, Naila Habib, Awais Adnan

Abstract:

The paper presents the Technique for Image Segmentation Using K-Means Clustering Classification. The presented algorithms were specific, however, missed the neighboring information and required high-speed computerized machines to run the segmentation algorithms. Clustering is the process of partitioning a group of data points into a small number of clusters. The proposed method is content-aware and feature extraction method which is able to run on low-end computerized machines, simple algorithm, required low-quality streaming, efficient and used for security purpose. It has the capability to highlight the boundary and the object. At first, the user enters the data in the representation of the input. Then in the next step, the digital image is converted into groups clusters. Clusters are divided into many regions. The same categories with same features of clusters are assembled within a group and different clusters are placed in other groups. Finally, the clusters are combined with respect to similar features and then represented in the form of segments. The clustered image depicts the clear representation of the digital image in order to highlight the regions and boundaries of the image. At last, the final image is presented in the form of segments. All colors of the image are separated in clusters.

Keywords: clustering, image segmentation, K-means function, local and global minimum, region

Procedia PDF Downloads 377

Authors: Prabesh Karki, Shyam Thapa Chettri, Bajarang Prasad Sah, Manoj Bhattarai, Sudeep Mishra

Abstract:

Introduction: Frontal sinus is frequently described as the most difficult sinus to access surgically due to its proximity to the cribriform plate, orbit, and anterior ethmoid artery. Frontal sinus surgery requires a detailed understanding of the cellular structure and FSDP unique to each patient, making high-resolution CT scans an indispensable tool to assess the difficulty of planned sinus surgery. International Frontal Sinus Anatomy Classification (IFAC) was developed to provide a more precise nomenclature for cells in the frontal recess, classifying cells based on their anatomic origin. Objectives: To assess the proportion of frontal cell variants defined by IFAC, variation with respect to age and gender. Methods: 54 cases were enrolled after a detailed clinical history, thorough general and physical examinations, and CT a report ordered in a film. Assessment and tabulation of the presence of frontal cells according to the IFAC analyzed. The prevalence of each cell type was calculated, and data were entered in MS Excel and analyzed using Statistical Package for the Social Sciences (SPSS). Descriptive statistics and frequencies were defined for categorical and numerical variables. Frequency, percentage, the mean and standard deviation were calculated. Result: Among 54 patients, 30 (55.6%) were male and 24 (44.4%) were female. The patient enrolled ranged from 18 to 78 years. Majority33.3% (n=18) were in age group of >50 years.According to IFAC, Agger nasi cells (92.6%) were most common, whereas supraorbital ethmoidal cells were least common 16 (29.6%). Prevalence of other frontoethmoidal cells was SAC- 57.4%, SAFC- 38.9%, SBC- 74.1%, SBFC- 33.3%, FSC- 38.9% of 54 cases. Conclusion: IFAC is an international consensus document that describes an anatomically precise nomenclature for classifying frontoethmoidal cells' anatomy. This study has defined the prevalence, symmetry and reliability of frontoethmoidal cells as established by the IFAC system as in other parts of the world.

Keywords: frontal sinus, frontoethmoidal cells, international frontal sinus anatomy classification

Procedia PDF Downloads 101

Authors: Asma Omri, Noureddine Benothman, Sofiane Sayahi, Fethi Tlili, Hichem Besbes

Abstract:

Cycling, a popular mode of transportation, can also be perilous due to cyclists' vulnerability to collisions with vehicles and obstacles. This paper presents an innovative cyclist safety system based on radar technology designed to offer real-time collision risk warnings to cyclists. The system incorporates a low-power radar sensor affixed to the bicycle and connected to a microcontroller. It leverages radar point cloud detections, a clustering algorithm, and a supervised classifier. These algorithms are optimized for efficiency to run on the TI’s AWR 1843 BOOST radar, utilizing a coarse classification approach distinguishing between cars, trucks, two-wheeled vehicles, and other objects. To enhance the performance of clustering techniques, we propose a 2-Level clustering approach. This approach builds on the state-of-the-art Density-based spatial clustering of applications with noise (DBSCAN). The objective is to first cluster objects based on their velocity, then refine the analysis by clustering based on position. The initial level identifies groups of objects with similar velocities and movement patterns. The subsequent level refines the analysis by considering the spatial distribution of these objects. The clusters obtained from the first level serve as input for the second level of clustering. Our proposed technique surpasses the classical DBSCAN algorithm in terms of geometrical metrics, including homogeneity, completeness, and V-score. Relevant cluster features are extracted and utilized to classify objects using an SVM classifier. Potential obstacles are identified based on their velocity and proximity to the cyclist. To optimize the system, we used the View of Delft dataset for hyperparameter selection and SVM classifier training. The system's performance was assessed using our collected dataset of radar point clouds synchronized with a camera on an Nvidia Jetson Nano board. The radar-based cyclist safety system is a practical solution that can be easily installed on any bicycle and connected to smartphones or other devices, offering real-time feedback and navigation assistance to cyclists. We conducted experiments to validate the system's feasibility, achieving an impressive 85% accuracy in the classification task. This system has the potential to significantly reduce the number of accidents involving cyclists and enhance their safety on the road.

Keywords: 2-level clustering, coarse classification, cyclist safety, warning system based on radar technology

Procedia PDF Downloads 85

Authors: Valentina Narvaez Gaitan, Laura Valentina Rodriguez Leguizamon, Ruben Dario Hernandez B.

Abstract:

This work describes a physiological signal emulation system that uses electromyography (EMG) signals obtained from muscle sensors in the first instance. These signals are used to extract their characteristics to model and emulate specific arm movements. The main objective of this effort is to develop a new biomedical software system capable of generating physiological signals through the use of embedded systems by establishing the characteristics of the acquired signals. The acquisition system used was Biosignals, which contains two EMG electrodes used to acquire signals from the forearm muscles placed on the extensor and flexor muscles. Processing algorithms were implemented to classify the signals generated by the arm muscles when performing specific movements such as wrist flexion extension, palmar grip, and wrist pronation-supination. Matlab software was used to condition and preprocess the signals for subsequent classification. Subsequently, the mathematical modeling of each signal is performed to be generated by the embedded system, with a validation of the accuracy of the obtained signal using the percentage of cross-correlation, obtaining a precision of 96%. The equations are then discretized to be emulated in the embedded system, obtaining a system capable of generating physiological signals according to the characteristics of medical analysis.

Keywords: classification, electromyography, embedded system, emulation, physiological signals

Procedia PDF Downloads 113

Authors: Maged A. Mogalli, Abdullah I. Al-Mansour, Seongkwan Mark Lee

Abstract:

In the last decades, population growth has gradually exceeded transportation infrastructure growth, and today’s transportation professionals are facing challenge on how to meet the mobility needs of a rising population especially in the absence of adequate public transport, as is the case in Saudi Arabia. The traffic movement congestion can be decreased by carrying out some appropriate alternative designs of interchanges such as diverging diamond interchange (DDI) and single diamond interchange (SPDI). In this paper, evaluation of newly implemented DDIs at the interchange of Makkah road with Prince Turki road and the interchange of King Khaled road with Prince Saud Ibn Mohammed Ibn Mugrin road in Riyadh city was carried out. The comparison between the DDI and SPDI is conducted by evaluating different measures of effectiveness (MOE) such as stop delay, average queue length, and number of stops. In this connection, each interchange type was evaluated for traffic flow at peak hours using micro-simulation program namely 'Synchro/SimTarffic' to measure its effectiveness such as stop delay, average queue length, and number of stops. The results of this study show that DDI provides a better result when compared with SPDI in terms of stope delay, average queue length, and number of stops. The stop delay for the SPDI is greater than DDI by three times. Also, the average queue length is approximately twice that of the SPDI when compared to the DDI. Furthermore, the number of stops for the SPDI is about twice as the DDI.

Keywords: single point diamond interchange, diverging diamond interchange, measures of effectiveness, simulation

Procedia PDF Downloads 262

Authors: Lavanya Ratnabala, Robinroy Peter, E. Y. A. Charles

Abstract:

This research paper addresses the challenges of exploration and navigation in unknown environments from an evolutionary swarm robotics perspective. Path formation plays a crucial role in enabling cooperative swarm robots to accomplish these tasks. The paper presents a method called the sub-goal-based path formation, which establishes a path between two different locations by exploiting visually connected sub-goals. Simulation experiments conducted in the Argos simulator demonstrate the successful formation of paths in the majority of trials. Furthermore, the paper tackles the problem of inter-collision (traffic) among a large number of robots engaged in path formation, which negatively impacts the performance of the sub-goal-based method. To mitigate this issue, a task allocation strategy is proposed, leveraging local communication protocols and light signal-based communication. The strategy evaluates the distance between points and determines the required number of robots for the path formation task, reducing unwanted exploration and traffic congestion. The performance of the sub-goal-based path formation and task allocation strategy is evaluated by comparing path length, time, and resource reduction against the A* algorithm. The simulation experiments demonstrate promising results, showcasing the scalability, robustness, and fault tolerance characteristics of the proposed approach.

Keywords: swarm, path formation, task allocation, Argos, exploration, navigation, sub-goal

Procedia PDF Downloads 44

Authors: Chithra A. V., Somoshree Datta, Sandeep Nithyanandan

Abstract:

Sign Language Recognition (SLR) is the process of interpreting and translating sign language into spoken or written language using technological systems. It involves recognizing hand gestures, facial expressions, and body movements that makeup sign language communication. The primary goal of SLR is to facilitate communication between hearing- and speech-impaired communities and those who do not understand sign language. Due to the increased awareness and greater recognition of the rights and needs of the hearing- and speech-impaired community, sign language recognition has gained significant importance over the past 10 years. Technological advancements in the fields of Artificial Intelligence and Machine Learning have made it more practical and feasible to create accurate SLR systems. This paper presents a distinct approach to SLR by framing it as a video classification problem using Deep Learning (DL), whereby a combination of Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) has been used. This research targets the integration of sign language recognition into healthcare settings, aiming to improve communication between medical professionals and patients with hearing impairments. The spatial features from each video frame are extracted using a CNN, which captures essential elements such as hand shapes, movements, and facial expressions. These features are then fed into an RNN network that learns the temporal dependencies and patterns inherent in sign language sequences. The INCLUDE dataset has been enhanced with more videos from the healthcare domain and the model is evaluated on the same. Our model achieves 91% accuracy, representing state-of-the-art performance in this domain. The results highlight the effectiveness of treating SLR as a video classification task with the CNN-RNN architecture. This approach not only improves recognition accuracy but also offers a scalable solution for real-time SLR applications, significantly advancing the field of accessible communication technologies.

Keywords: sign language recognition, deep learning, convolution neural network, recurrent neural network

Procedia PDF Downloads 32

Authors: Nalluri Lakshmi Ramu, C. Venkat Nihit, Narayana Kumar, Dillep

Abstract:

Masonry bridges are the iconic heritage transportation infrastructure throughout the world. Continuous increase in traffic loads and speed have kept engineers in dilemma about their structural performance and capacity. Henceforth, research community has an urgent need to propose an effective methodology and validate on real-time bridges. The presented research aims to assess the structural health of an Eighty-year-old masonry railway bridge in India using wireless accelerometer sensors. The bridge consists of 44 spans with length of 24.2 m each and individual pier is 13 m tall laid on well foundation. To calculate the dynamic characteristic properties of the bridge, ambient vibrations were recorded from the moving traffic at various speeds and the same are compared with the developed three-dimensional numerical model using finite element-based software. The conclusions about the weaker or deteriorated piers are drawn from the comparison of frequencies obtained from the experimental tests conducted on alternative spans. Masonry is a heterogeneous anisotropic material made up of incoherent materials (such as bricks, stones, and blocks). It is most likely the earliest largely used construction material. Masonry bridges, which were typically constructed of brick and stone, are still a key feature of the world's highway and railway networks. There are 1,47,523 railway bridges across India and about 15% of these bridges are built by masonry, which are around 80 to 100 year old. The cultural significance of masonry bridges cannot be overstated. These bridges are considered to be complicated due to the presence of arches, spandrel walls, piers, foundations, and soils. Due to traffic loads and vibrations, wind, rain, frost attack, high/low temperature cycles, moisture, earthquakes, river overflows, floods, scour, and soil under their foundations may cause material deterioration, opening of joints and ring separation in arch barrels, cracks in piers, loss of brick-stones and mortar joints, distortion of the arch profile. Few NDT tests like Flat jack Tests are being employed to access the homogeneity, durability of masonry structure, however there are many drawbacks because of the test. A modern approach of structural health assessment of masonry structures by vibration analysis, frequencies and stiffness properties is being explored in this paper.

Keywords: masonry bridges, condition assessment, wireless sensors, numerical analysis modal frequencies

Procedia PDF Downloads 171

Authors: Madushani Rodrigo, Banuka Athuraliya

Abstract:

In today's world of medical diagnosis and prediction, machine learning stands out as a strong tool, transforming old ways of caring for health. This study analyzes the use of machine learning in the specialized domain of sports medicine, with a focus on the timely and accurate detection of bone fractures in cricket athletes. Failure to identify bone fractures in real time can result in malunion or non-union conditions. To ensure proper treatment and enhance the bone healing process, accurately identifying fracture locations and types is necessary. When interpreting X-ray images, it relies on the expertise and experience of medical professionals in the identification process. Sometimes, radiographic images are of low quality, leading to potential issues. Therefore, it is necessary to have a proper approach to accurately localize and classify fractures in real time. The research has revealed that the optimal approach needs to address the stated problem and employ appropriate radiographic image processing techniques and object detection algorithms. These algorithms should effectively localize and accurately classify all types of fractures with high precision and in a timely manner. In order to overcome the challenges of misidentifying fractures, a distinct model for fracture localization and classification has been implemented. The research also incorporates radiographic image enhancement and preprocessing techniques to overcome the limitations posed by low-quality images. A classification ensemble model has been implemented using ResNet18 and VGG16. In parallel, a fracture segmentation model has been implemented using the enhanced U-Net architecture. Combining the results of these two implemented models, the FracXpert system can accurately localize exact fracture locations along with fracture types from the available 12 different types of fracture patterns, which include avulsion, comminuted, compressed, dislocation, greenstick, hairline, impacted, intraarticular, longitudinal, oblique, pathological, and spiral. This system will generate a confidence score level indicating the degree of confidence in the predicted result. Using ResNet18 and VGG16 architectures, the implemented fracture segmentation model, based on the U-Net architecture, achieved a high accuracy level of 99.94%, demonstrating its precision in identifying fracture locations. Simultaneously, the classification ensemble model achieved an accuracy of 81.0%, showcasing its ability to categorize various fracture patterns, which is instrumental in the fracture treatment process. In conclusion, FracXpert has become a promising ML application in sports medicine, demonstrating its potential to revolutionize fracture detection processes. By leveraging the power of ML algorithms, this study contributes to the advancement of diagnostic capabilities in cricket athlete healthcare, ensuring timely and accurate identification of bone fractures for the best treatment outcomes.

Keywords: multiclass classification, object detection, ResNet18, U-Net, VGG16

Procedia PDF Downloads 126